s2fieldsofstudy: Biology Medicine

text:
FDG uptake, a surrogate of tumour hypoxia?

Introduction Tumour hyperglycolysis is driven by activation of hypoxia-inducible factor-1 (HIF-1) through tumour hypoxia. Accordingly, the degree of 2-fluro-2-deoxy-d-glucose (FDG) uptake by tumours might indirectly reflect the level of hypoxia, obviating the need for more specific radiopharmaceuticals for hypoxia imaging. Discussion In this paper, available data on the relationship between hypoxia and FDG uptake by tumour tissue in vitro and in vivo are reviewed. In pre-clinical in vitro studies, acute hypoxia was consistently shown to increase FDG uptake by normal and tumour cells within a couple of hours after onset with mobilisation or modification of glucose transporters optimising glucose uptake, followed by a delayed response with increased rates of transcription of GLUT mRNA. In pre-clinical imaging studies on chronic hypoxia that compared FDG uptake by tumours grown in rat or mice to uptake by FMISO, the pattern of normoxic and hypoxic regions within the human tumour xenografts, as imaged by FMISO, largely correlated with glucose metabolism although minor locoregional differences could not be excluded. In the clinical setting, data are limited and discordant. Conclusion Further evaluation of FDG uptake by various tumour types in relation to intrinsic and bioreductive markers of hypoxia and response to radiotherapy or hypoxia-dependent drugs is needed to fully assess its application as a marker of hypoxia in the clinical setting.

Introduction
The most important non-surgical treatment for cancer is radiotherapy; of the 50% of cancer patients that can be currently cured, 70% receive radiation either as monotherapy or as part of a multimodality treatment. For a number of tumours such as squamous cell carcinoma of the head and neck or cervix carcinoma, radiotherapeutic treatment may fail due to the presence of tumour hypoxia (pO 2 <5 mmHg) which decreases the availability of oxygen free radicals that are necessary to induce sufficient DNA damage to cause cell death [1][2][3][4]. A non-invasive imaging technique allowing the identification and quantification of hypoxia in these patients may identify those patients that might benefit from concomitant radiosensitisation therapy or from dose escalation.
During the past two decades, several hypoxia-targeting radiopharmaceuticals have been developed. The chemical basis for most of these compounds, e.g. 18 F-misonidazole ( 18 F-MISO) and 18F/123I-iodoazomycin arabinoside ( 18 F-AZA/ 123 I-AZA, respectively), has been to incorporate a 2-nitroimidazole moiety to act as a bioreductive molecule accepting a single electron and producing a free radical anion, which, after further reduction, is then incorporated into cell constituents under hypoxic conditions [5,6]. For other non-imidazole moiety containing hypoxia-targeting radiopharmaceuticals, e.g. 99m Tc-2,2′-(1,4-diaminobutane) bis(2-methyl-3-butanone)dioxime ( 99m Tc-HL91) and 62 Cudiacetyl-bis(N-4-methylthiosemicarbazone) ( 62 Cu-ATSM), the mechanisms of tumour accumulation is not wellclarified, although it is generally accepted that the 99m Tc and 62 Cu complexes are bioreducible groups themselves. To find routine clinical application, these radiopharmaceuticals should be widely available, provide meaningful averages over tumour volumes and allow for administration, before, during and after the investigation of tumour oxygenation and re-oxygenation. Unfortunately, their synthesis, however, requires either the presence of a cyclotron or specialised in-house radiopharmacy expertise, facilities still unavailable to most nuclear medicine departments [5,6]. Contrary to hypoxia-targeting radiopharmaceuticals, 2fluro-2-deoxy-D-glucose (FDG) is widely available. The basis for the application of FDG PET imaging in oncology is the upregulation of glucose transporters (GLUTs) and glycolytic enzymes also known as the Warburg effect or tumour hyperglycolysis [7,8]. It is important to note that tumour hyperglycolysis is driven by the activation of hypoxia-inducible factor-1 (HIF-1) either through genetic alterations or changes of levels of growth factors and also via tumour hypoxia. Accordingly, the degree of FDG uptake by tumours might indirectly reflect the level of hypoxia, obviating the need for more specific radiopharmaceuticals for hypoxia imaging. This paper reviews available data on the relationship between hypoxia and FDG uptake by tumour tissue in vitro and in vivo.

Cell line findings
The uptake of 3 H-FDG into two human tumour cell lines (HTB 63 melanoma and HTB 77 IP3 ovarian carcinoma) was determined after exposure to differing oxygen atmospheres ranging from 0% to 20% O 2 for varying time periods by Clavo et al. [9]. FDG uptake in both melanoma and ovarian carcinoma cell lines increased significantly (39.6±6.7% and 36.7±9%, respectively) over basal (20% O 2 ) conditions when cells were exposed to a mild hypoxic environment (5% O 2 ) for 1.5 h. With a 4-h exposure to 1.5% O 2 , the increase in FDG uptake was greater at 52.3± 8.9% and 43.5±19%, respectively. With 4 h of anoxia, the increase in FDG uptake over basal conditions was 42.7±10% and 63.3±13.7% for melanoma and ovarian carcinoma cells, respectively. Membrane transport of 3-O-methylglucose (3-OMG) was increased by hypoxia for melanoma and ovarian carcinoma. Immunochemical assays for Glut-1 showed an increase in the membrane expression of the Glut-1 transporter in cells exposed to hypoxia.
Hypoxic accumulation of 2-[5,6-3H]fluoro-2-deoxy-Dglucose was evaluated in two cell lines (UT-SCC-5 and UT-SCC-20) obtained from patients with squamous cell carcinoma of the head and neck by Minn et al. [10]. Both cell lines were exposed to decreasing oxygen atmosphere (20%, 1.5%, or 0% O 2 ) for 6 h, after which they were incubated for a further 1 h with tritiated FDG. An anoxic atmosphere resulted in a mean increase of 3 H-FDG uptake of 120% and 46% over a baseline 20% oxygen atmosphere for UT-SCC-5 and UT-SCC-20A, respectively.
Similarly, a more than twofold increase (2.53±0.79; p< 0.005) in 3 H-FDG uptake was observed under hypoxic conditions by Burgman et al. by MCF-7 breast cancer cells, but no changes in the cellular levels of glucose transporter proteins (GLUT1 and GLUT3) or hexokinase were observed [11]. A reducing agent, dithiothreitol (DTT), also caused an increase in 3 H-FDG uptake but failed to affect uptake under hypoxic conditions. This indicates that the mechanisms by which hypoxia and DTT affect 3 H-FDG uptake might be the same. The oxidising agent chloromercuribenzenesulphonic acid (pCMBS) had no effect on 3 H-FDG uptake under normoxic conditions but counteracted the effect of hypoxia. DTT caused an increase in glucose transporter activity, whereas it had no effect on hexokinase activity. pCMBS had no effect on either glucose transporter activity or hexokinase activity. The authors concluded that hypoxia-induced increase in 3 H-FDG uptake in MCF7 cells is the result, in part, of an increase in glucose transporter activity resulting from the modification (reduction) of thiol group(s) in the glucose transport protein(s) and that the modulation of hexokinase activity is probably not involved in the hypoxia-induced increase in 3 H-FDG uptake in these cells.
Pedersen et al. studied the relationship between 18 Flabelled FDG uptake and expression of glucose transporters (GLUTs 1, 2 and 3) in two human small cell lung cancer (SCLC) lines CPH 54A and CPH 54B [12]. Changes in the expression of GLUTs and vascular endothelial growth factor (VEGF) during 12, 18, and 24 h of severe hypoxia in vivo (xenografts) and in vitro (cell cultures) were recorded for both tumour lines. There were higher levels of Glut-1 protein in 54B and a correspondingly higher FDG uptake in this tumour line (p<0.001). During hypoxia, a significant upregulation of VEGF mRNA, GLUT-1 mRNA and Glut-1 and Glut-3 proteins occurred with a distinctly different time course in the two cell lines. A similar coupregulation of GLUT and VEGF was seen in hypoxic tumours of both lines. There were no significant changes of HIF-1alpha mRNA during hypoxia in either of the cell lines.
Hara et al. monitored the uptake of [ 18 F] 2-fluoro-2deoxyglucose ( 18 F-FDG) in androgen-independent PC-3 cells and androgen-sensitive LNCaP cells under aerobic or anoxic conditions [13]. The effect of androgen depletion was also examined. After 4 h of anoxia, PC-3 cells showed an 85% increase in FDG uptake whereas LNCaP cells showed a 212% increase in FDG uptake. Of interest, androgen depletion resulted in a marked decrease in the uptake of 18

F-FDG in LNCaP cells but not in PC-3 cells.
Finally, Oswald et al. studied FDG uptake in three tumour cell lines, namely, HT-29 (colorectal), FaDu (SCCHN) and MCF-7 (HT-29 and FaDu) and in three primary endothelial cell lines cultured at normoxic and hypoxic conditions [14]. In comparison to normoxic conditions, cellular uptake of 18 F-FDG was significantly increased at hypoxic conditions in two of the three tumour cell lines under study and all three endothelial cell lines.

Animal models
Dearling et al. injected mice (MF1) bearing LS174T human tumour xenografts premixed 18 F-FDG (100 MBq), 14 C-2DG (0.37 MBq) and pimonidazole hydrochloride (60 mg/kg) [15]. After 30, 60 and 120 min, tissues (n=4) were taken and counted for whole-body biodistribution. Tumours were frozen, sectioned and exposed to phosphor image plates to obtain a quantitative digital image of radionuclide distribution. Sections were then stained to reveal tumour pathophysiology. Viable tissues were separated from necrotic tissues based on haematoxylin and eosin staining and manually based on regions of interest. Within these regions, the amount of hypoxic (brown staining or pimonidazole-positive) and normoxic tissue (blue staining or pimonidazole-negative) was assessed using an automated colour-discriminating in-house written software programme. Whole-body biodistributions and tumour microdistribution for the tracers were similar (r=0.85±0.10, p<0.0001 for tumour uptake). When the tumours were divided into viable and necrotic regions, the ratio of mean counts per pixel was 1.96 (p<0.0001), whereas for hypoxic versus normoxic regions, it was 1.26 (p<0.0001). There was no significant difference in selectivity for hypoxia between the tracers (p= 0.86). This study confirmed the selectivity of deoxyglucose for viable cells over necrotic regions and for hypoxic cells over normoxic regions in vivo.
FDG PET and 18 F-FMISO PET imaging was performed with a dedicated small-animal PET scanner NanoPET (Oxford Positron Systems) in ten different human tumour xenografts in nude mice and B16 melanoma tumours in syngeneic Balb/c mice by Wyss et al. The pattern of normoxic and hypoxic regions within the human tumour xenografts, as imaged by 18 F-FMISO and 18 F-FDG proved similar. In addition, the uptake values of FDG and parameters used to quantify FMISO accumulation exhibited a strong positive correlation [16].
Zanzonico et al. studied the intra-tumoural distributions of sequentially administered F18-fluoro-deoxyglucose (FDG) and the hypoxia tracer 18 F-fluoromisonidazole (FMISO) were compared in nude rats with a hindlimb R3327-AT anaplastic rat prostate tumour xenograft and a hindlimb FaDu human squamous cell carcinoma (each up to 20×20×30 mm in size) rats by registered micro-PET imaging with positioning of each animal in a customfabricated whole-body mould [17]. The registered micro-PET images unambiguously demonstrated grossly similar although not identical distributions of FDG and FMISO in the tumours-a high-activity rim surrounding a loweractivity core. There were, however, subtle but possibly significant differences in the intra-tumoural distributions of  [19]. Parameters of pO 2 -polarography were the relative frequency of pO 2 readings ≤2.5, ≤5 and ≤10 mmHg, respectively, and the mean and median pO 2 in lymph nodes under study. Whilst a moderate correlation of the maximum standardised uptake value (SUVmax) of FDG with the tumour and/or lymph node to blood ratio of FMISO at 2 h (r=0.53, p< 0.05) was observed, SUV of FDG was similar in hypoxic and normoxic tumours as defined by pO 2 -polarography (6.9±3.2 vs 6.2±3.0, NS) and the FDG uptake was not correlated with the results of pO 2 -polarography whereas FMISO uptake was (r=0.80, p<0.001) [19]. These results were later on confirmed by the same authors in an extended series of 36 patients [20].
Thorwarth et al. studied 12 patients suffering from advanced head and neck cancer (HNC) that underwent FDG and FMISO PET examinations before RT treatment. In this series, tumour volume was defined in two steps; first through region growing, using a threshold of 60% of the maximum FDG uptake value followed by an extension of this volume by 3 voxels in each direction yielding the final tumour volume [21]. The percentage of voxels of each tumour that presented FMISO SUV values at 2 h pi. larger than 1.4 defined the hypoxic fraction of each tumour. The authors also performed a scatter-plot based voxel-by-voxel FDG-FMISO correlation for each patient for the defined tumour volumes and assessed whether the slope of the regression line (m) and the scatter width of the scatter plots (sigma) allowed to stratify the patient group with respect to treatment outcome. No general correlation of enhanced glucose metabolism and FMISO uptake was observed. In seven out of the 12 patients studied, scatter patterns for the primary tumour were very narrow suggesting a functional dependence of FDG and FMISO uptake. In the two patients in whom lymph nodes were also included for analysis, widely dispersed data points of FDG and FMISO SUVs were found for lymph nodes. Finally, whereas the maximum uptake of FMISO (p=0.045) showed borderline significance for stratifying the patient group, FDG positive tumour volume, hypoxic fraction, maximum FDG uptake and m were not significant. Sigma turned out to be the most significant variable (p=0.008) to predict treatment success probabilities.
In a study by Rajendran et al., the mean correlation coefficient between FMISO and FDG uptake by primary SCCHN on a pixel-by-pixel basis per tumour was 0.62 [22]. In this study, patients suffering from different T-stages were included and FDG uptake by the primary tumour covered a wide range (FDG SUV max=2.9-25.4).

Non-small cell lung carcinoma (NSCLC)
Cherk et al. prospectively studied 21 patients with suspected or biopsy-proven NSCLC that underwent both FMISO and FDG PET [23]. Seventeen patients subsequently underwent surgery with analysis performed for tumour markers of angiogenesis and hypoxia. In the 17 patients with resectable NSCLC (13 men, 4 women; age range=51-77 years), the mean FMISO uptake in tumour was significantly lower than that of FDG uptake (p<0.0001) and showed no correlation with FDG uptake (r=0.26). The hypoxic cell fraction of primary NSCLC was consistently low, and there was no significant correlation in NSCLC between hypoxia and glucose metabolism in NSCLC assessed by 18 [26]. There was 100% concordance as to the absence or presence of uptake of both tracers in all tumours under study with five patients displaying a lack of uptake of either tracer preferentially in low-grade tumours. The pattern of uptake of both tracers within positive tumours was, however, different with areas of marked FMISO uptake often corresponding to areas of only slightly increased FDG uptake.
Finally, no significant correlation was found between FDG SUV max values and FMISO SUVmax values in patients suffering from soft tissue sarcoma, breast cancer or glioblastoma multiforme (r values, respectively, 0.47, 0.38 and 0.32; number of patients studied, respectively, 11, 7 and 5) [22].

Discussion
Solid tumours consist of regions with large numbers of hypoxic cells compared with the surrounding normal tissue with substantial heterogeneity within a given tumour and among tumours of the same histology [7,8]. The underlying process involved in generating a hypoxic micro-environment is not well-understood but likely include various mechanisms of impaired intra-tumoural oxygen delivery and consumption. In particular, tumour hypoxia seems to arise from the stochastic development of vasculature during tumour growth. Tumour blood vessels are highly irregular, lack smooth muscles and innervation, may have an incomplete endothelial lining and basement membrane and often develop from post capillary venules, creating severe longitudinal pO 2 gradients within the vessels themselves. Because of these factors, a significant proportion of tumour cells lie beyond the diffusion distance of oxygen, rendering them chronically or diffusion-limited hypoxic. In addition, tumour hypoxia can also occur acutely because of transient occlusion, narrowing of vessels, arteriolar vasomotion and variation in red cell traffic patterns. Models based on data generated from experimental studies suggest that substantial volumes of solid tumours are exposed to cyclic episodes of acute or transient hypoxia. Both acute and chronic hypoxia are present at any time in tumour tissues, the latter form predominating, and their influence on FDG uptake has been studied by various authors.
In pre-clinical in vitro studies, acute hypoxia was consistently shown to increase FDG uptake by normal and tumour cells within a couple of hours after onset [9][10][11][12][13]. The time course and extent of uptake proved very different from one tumour cell line to another. In addition, in those few studies that looked at associated changes in glucose transporter expression and hexokinase activity, a significant increase in the number of glucose transporters but not hexokinase activity was evidenced which may be due to recycling of internalised receptors to the cell membrane or to a decreased rate of internalisation of GLUTs. In addition, in one study by Burgman et al., it was suggested that modification (reduction) of thiol group(s) present on GLUTs through hypoxia may increase the kinetics of glucose transport by GLUTs [11]. In the one study that performed delayed assessment of glucose uptake after 18-24 h of acute hypoxia, mRNA analysis was performed revealing increased levels of mRNA of amongst others GLUTs and VEGF [12]. Overall, these findings suggest a rapid onset response of cells to hypoxia with mobilisation or modification of GLUTs optimising glucose uptake, followed by a delayed response with increased rates of transcription of GLUT mRNA.
In pre-clinical imaging studies on chronic hypoxia that compared FDG uptake by tumours grown in rats or mice to uptake by FMISO, the pattern of normoxic and hypoxic regions within the human tumour xenografts, as imaged by FMISO, largely correlated with glucose metabolism although minor locoregional differences could not be excluded [16,17]. This finding suggests a compensatory mechanism for the reduced delivery of glucose to hypoxic tumour cells by augmented tissue extraction owing to elevated glucose transporter activity under hypoxia in the tumour models under study. As opposed to FMISO, in the one study that compared 64 Cu-ASTM to FDG uptake by tumour tissues, findings proved discordant [18]. In all four tumour models under study, LLC1, colon26, Meth-A and B16, FGD and 64 Cu-ATSM were accumulated with different gradation forming a unique pattern of a high 64 Cu-ATSM region surrounding a high FDG region within a tumour mass which in turn surrounded a necrotic core. The imaging results obtained with 64 Cu-ATSM are at the least to say surprising and quite opposite to the general model of tumour hypoxia that includes a peripheral viable core, a central necrotic core and a hypoxia rim in between. Contrariwise, the images obtained with FDG proved similar to those obtained by Dearling et al. who compared pimonidazole staining with FDG uptake in a MCF-7 tumour model and found similar results for both tracers [15]. Due to the lack of a gold standard for hypoxia, e.g. polarography, no definitive conclusions can be derived from this study.
Data reporting on the relationship between FDG uptake and tumour hypoxia in the clinical setting are limited. In patients suffering from squamous cell carcinoma of the head and neck, studies have looked at the relationship between hypoxia and FDG uptake in both lymph nodes (one cumulative series of 36 patients by Gagel et al.) and primary tumours (two series of patients suffering from SCCHN, respectively, one on 12 patients by Thorwarth et al. and one reporting on 38 patients by Rajendran et al.) with seemingly conflicting results [19][20][21][22]. Whereas no relationship was found between FDG uptake and hypoxia in lymph nodes and in the smaller study in primary tumours by Thorwarth et al., a significant relationship was found in primary tumours in the study by Rajendran et al. [21,22]. Based on available data, hypoxic regions appear to be distributed homogeneously in cervical lymph node metastases from SCCHN and there is no gradient of oxygenation, contrary to what is seen in primary tumours [27]. In addition, the level of hypoxia is inversely correlated with lymph node perfusion, suggesting that hypoxia in invaded lymph nodes is flow-related rather than diffusion-related as is the case in primary tumours [27]. It is currently unknown whether the degree of hypoxia is also related to the level of lymph node involvement or not. The lack of such a relationship might also explain the lack of a relationship between hypoxia and FDG accumulation in lymph nodes as demonstrated in the series by Gagel  In the few studies that assessed FDG uptake versus FMISO uptake in a limited number of patients suffering from NSCLC, glioblastoma multiforme or breast cancer, no correlation was found between both variables [22][23][24].
Based on recent data, it appears that HIF-1 expression may also be observed in non-hypoxic tumour regions, suggesting that not only the hypoxic micro-environment but also non-hypoxic factors such as genetic alterations or levels of growth factors affect HIF-1 levels and thus also indirectly affect glucose metabolism [7,8]. Accordingly, it cannot be excluded that the existence of a correlation or lack of correlation between hypoxia and FDG accumulation is tumour type-dependent.
To conclude, further evaluation of FDG uptake by various tumour types in relation to intrinsic and bioreductive markers of hypoxia and response to radiotherapy or hypoxia-dependent drugs is needed to fully assess its role as a marker of hypoxia in the clinical setting. In particular, the relationship between HIF-1 activation and hypoxia in various human malignancies warrants further study. Whilst from a biological point of view, FDG uptake by tumour cells reflects a different aspect of tumour biology when compared to the uptake of e.g. FMISO or other hypoxiatargeting radiopharmaceuticals in those tumours where HIF-1 activation is mainly hypoxia-driven, the degree of FDG uptake may be a surrogate marker of hypoxia. Inversely, in those tumours where HIF-1 activation occurs mainly independently of hypoxia, specific hypoxia-targeting radiopharmaceuticals such as FMISO or 64 Cu-ATSM may be required to non-invasively image tumour hypoxia.
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extfieldsofstudy: Biology Medicine

text:
Growth of Yersinia pseudotuberculosis in human plasma: impacts on virulence and metabolic gene expression

Background In man, infection by the Gram-negative enteropathogen Yersinia pseudotuberculosis is usually limited to the terminal ileum. However, in immunocompromised patients, the microorganism may disseminate from the digestive tract and thus cause a systemic infection with septicemia. Results To gain insight into the metabolic pathways and virulence factors expressed by the bacterium at the blood stage of pseudotuberculosis, we compared the overall gene transcription patterns (the transcriptome) of bacterial cells cultured in either human plasma or Luria-Bertani medium. The most marked plasma-triggered metabolic consequence in Y. pseudotuberculosis was the switch to high glucose consumption, which is reminiscent of the acetogenic pathway (known as "glucose overflow") in Escherichia coli. However, upregulation of the glyoxylate shunt enzymes suggests that (in contrast to E. coli) acetate may be further metabolized in Y. pseudotuberculosis. Our data also indicate that the bloodstream environment can regulate major virulence genes (positively or negatively); the yadA adhesin gene and most of the transcriptional units of the pYV-encoded type III secretion apparatus were found to be upregulated, whereas transcription of the pH6 antigen locus was strongly repressed. Conclusion Our results suggest that plasma growth of Y. pseudotuberculosis is responsible for major transcriptional regulatory events and prompts key metabolic reorientations within the bacterium, which may in turn have an impact on virulence.

Background
The Gram-negative bacterium Y. pseudotuberculosis is a human enteropathogen which is able to cross the intestinal mucosa through the M cells in Peyer's patches and thus infect the underlying tissues (causing ileitis and mesenteric lymphadenitis). However, in elderly or debilitated individuals (those suffering from malignancies, immunodeficiencies, chronic liver diseases or diabetes mellitus, for example), the organism frequently gains access to the bloodstream and can cause an often fatal septicemia [1,2]. Known Y. pseudotuberculosis virulence genes are transcriptionally regulated by temperature -most probably in order to adapt to the bacterium's life cycle outside and inside the host. Regulation by the omnipresent thermal stimulus can be modulated (via a wide range of mechanisms) by signals such as pH, other ion concentrations and nutrient availability (reviewed in [3]). This allows bacterial pathogens to (i) adapt their gene transcription profiles in response to environmental cues sensed during the course of infection and (ii) express the most appropriate virulence factors at the expense of useless (or even detrimental) ones.
To date, the transcriptional gene regulation occurring when Y. pseudotuberculosis enters the human bloodstream has only been inferred indirectly from in vivo results in rodent models of infection [4,5] and in vitro gene transcription studies. The in vitro regulation of certain Yersinia virulence loci has mainly been analyzed with respect to single growth parameter changes mimicking the environmental signals known (or assumed) to be detected by bacteria in blood, such as iron scarcity, oxygen tension and pH [6,7]. In the present work, we have adopted an intermediate approach by comparing the overall gene expression profiles of Y. pseudotuberculosis grown in human plasma and in Luria-Bertani broth. We then compared the observed variations with those recently published for Y. pestis [8], an almost genetically identical pathogen which, however, causes plague -one of the most severe systemic infections in humans and other mammals.

Results and discussion
The genome of Y. pseudotuberculosis strain IP32953 has been recently deciphered: it contains 3,951 coding sequences (CDSs), of which 99 are borne by the virulenceassociated plasmid pYV and 43 are carried by a 27-kb cryptic plasmid. Only around 49% of CDSs encode a product with a putative or proven function [9]. To gain insight into the transcriptional regulation of virulence and metabolism genes that takes place when Y. pseudotuberculosis enters and multiplies in the bloodstream, we compared the transcriptome of IP32953 grown in human plasma to the one of the same strain grown in Luria-Bertani (LB). To this end, we prepared macroarrays composed of 3,674 PCR fragments of ≈ 400-base pairs (bp), covering 96% of IP32953's CDSs and used them as described elsewhere [8] and in the Methods section. Briefly, in three independent cultures, total RNA was extracted from IP32953 cells grown in LB broth or human plasma, in the exponential or stationary phase and at 28°C or 37°C. Macroarray probing was performed three times with independently retrotranscribed and 33 P-radiolabeled RNA samples from each of the eight growth combinations. After macroarray imaging, hybridization intensity data were log-transformed and normalized using a simple median normalization method. Relative data have been deposited in the Genoscript database http:// genodb.pasteur.fr/cgi-bin/WebObjects/GenoScript in accordance with standards of the Microarray Gene Expression Data Society (MGED). An analysis of variance (ANOVA) was carried out independently for each gene, with the three biological factors of variation (medium, temperature and growth phase) as fixed effects. This statistical approach allowed us to evaluate the transcriptional variations induced by each factor for the dataset as a whole. Thus, three ratios (corresponding to each parameter) and associated p-values were calculated for each gene. Inter-condition transcriptional differences were considered to be statistically significant if the p-value was below 0.05. Representative macroarray hybridization results were confirmed by qRT-PCR on stored RNA samples, using the constitutively expressed YPTB0775 gene (spot ID YPO3356 and coding for the outer membrane lipoprotein NplD) as a reference (Additional file 1). Since the physiological status of the bacterium during host infection is unknown, we focused our analysis on genes regulated by the temperature and/or the medium in both the exponential and stationary phases. All Y. pseudotuberculosis transcriptional variations discussed herein were compared with those of their respective Y. pestis orthologs and are summarized in Table 1. Y. pseudotuberculosis IP32953 genes regulated at the transcriptional level by growth temperature and/or medium are listed in Tables 2 and 3.
Free iron limitation is a well-known stimulus encountered by bacteria in plasma [10,11]. As expected, IP32953 genes required for iron storage (such as the ferritin-encoding gene ftnA [12] (Fig. 1)) were found to be downregulated in plasma. Transcriptional upregulation of most iron uptake systems (along with accessory protein-encoding genes tonB, exbB and exbD) (Fig. 1) is also consistent with this condition and is in agreement with the recent findings in Y. pestis [8]. As iron is used as a cofactor by numerous enzymes (mostly when complexed with sulfur), the metal is essential for a broad range of metabolic processes. Besides activation of iron homeostasis systems, lack of iron is also expected to be associated with a dramatic decrease in the transcription of genes encoding such enzymes, with the underlying goal of lowering iron consumption. This situation is exemplified by the katA gene                             that encodes catalase (a ferric enzyme involved in oxidative stress defense), whose transcription is decreased in both Y. pestis and Y. pseudotuberculosis during growth in plasma (Fig. 1). However, the increase in transcription of the bio locus (required for biotin synthesis [13]) and observed in both species) suggests that differential genetic control of a subset of iron-dependent enzymes may favor supply of this metal to the pathways that are most important for bacterial survival (and thus presumably at the expense of other, less critical ones). Furthermore, the impact of transcriptional downregulation on reorientation of metabolic fluxes may be minimized by the con-comitant activation of genes coding for isoenzymes that are better suited to this situation.
One example is that of the manganese-and iron-dependent superoxide dismutase genes (i.e sodA and sodB), which are Fur-activated and -repressed, respectively ( Fig. 1) in both Y. pestis and Y. pseudotuberculosis. Similarly, the class Ib ribonucleotide reductase (RNR)-encoding genes (nrdHIEF) are probably important for bacterial life in plasma, since they were found to be upregulated at the expense of those in classes III (nrdDG) and Ia (nrdAB) ( Table 2) -even though all three classes are equally involved in generating the synthetic precursors for DNA. The fact that only the first class is Fur-activated [14] is consistent with this observation. Similar variations have also been recorded in Y. pestis [8]. However, whereas purine/ pyrimidine metabolism has been shown to be essential for Y. pestis virulence [15], the role of this metabolic pathway in the physiopathology of Y. pseudotuberculosis has not yet been investigated. Along with class 1b RNRs, more than half of the enzymes in the tricarboxylic acid cycle (TCA) are known to be catalytically iron-dependent and/ or believed to be transcriptionally activated by Fur [16]. Accordingly, and in line with transcriptome data from Y.
pestis, we observed that transcription of these genes fell significantly when Y. pseudotuberculosis was grown in plasma.
In contrast to the low availability of iron in blood, glucose is readily available in this biological fluid and at a higher concentration (approx. 7 mM) than in LB broth. When Y. pseudotuberculosis was cultured in plasma, genes involved in glycolysis and the upstream, sugar-supplying, phosphoenolpyruvate-dependent systems were found to be upregulated, as depicted in Fig. 2. This finding is reminiscent of an aerobic phenomenon referred to as "glucose Medium-and temperature-dependent differential expression of Y. pseudotuberculosis chromosomal genes involved in virulence and/or iron uptake & storage Figure 1 Medium-and temperature-dependent differential expression of Y. pseudotuberculosis chromosomal genes involved in virulence and/or iron uptake & storage. Significant (p < 0.05) upshifts (yellow to red scale) or downshifts (blue scale) in individual gene transcription levels when bacteria were grown in human plasma versus LB (triangles) and/or at 37°C versus 28°C (squares) are indicated by the color scale bar. Genes encoding iron uptake/storage systems, virulence factors and their regulators are symbolized by gray, white and black arrows, respectively. Nomenclature used for gene designation correspond to the Y. pseudotuberculosis IP32953 genome annotation. Mean fold changes in transcription and p-values are indicated in Table 2.
Medium-dependent differential expression of genes coding for enzymes putatively involved in Y. pseudotuberculosis glycolysis and the tricarboxylic acid cycle (TCA cycle) Figure 2 Medium-dependent differential expression of genes coding for enzymes putatively involved in Y. pseudotuberculosis glycolysis and the tricarboxylic acid cycle (TCA cycle). Significant (p < 0.05) upshifts (yellow to red scale) or downshifts (blue scale) in individual gene transcription levels in human plasma versus LB is indicated by the color scale bar.
Open boxes indicate genes whose expression levels did not vary significantly (p > 0.05). Although considered as not significant by statistical analysis of macroarray data (p = 0.053), transcriptional upregulation of aceB in human plasma was confirmed by qRT-PCR. Abbreviations: Ac-CoA: acetyl coenzyme A; PEP: phosphoenolpyruvate. Mean fold changes in transcription and pvalues are indicated in Table 2. overflow metabolism"; this consists in channeling the carbon flow towards acetate formation instead of citrate formation, in order to prevent the excessive accumulation of NADH that would otherwise result from very high glucose consumption rates [17]. However, one main feature of glucose overflow in E. coli is acetate accumulation due to a strong transcriptional repression of the glyoxylate shunt aceBAK operon [18]. Interestingly, at least the first two of these genes are not down-but are up-regulated in Y. pseudotuberculosis (Fig. 2, Additional file 1), suggesting a need for this species to limit acetate overloads. The continuous de-repression of these genes (due to inactivation of the IclR repressor) suggests that this might also be the case in Y. pestis. These pathways are controlled by complex and finely balanced networks involving numerous pleiotropic regulators, including Fur, Crp, Fnr and ArcA [16,19]. This unexpected upregulation may well result from the combination of both high glucose and low iron levels in plasma. Whether this occurs through the strong transcriptional repression observed with both fnr and arcA remains to be addressed in future experiments.
Temperature upshift is typically considered to be the main signal indicating to bacteria that they have entered the host; this hypothesis is supported by the thermal dependency of almost all Y. pseudotuberculosis virulence genes and also many of the latter's regulators [3]. Several of these genes were also found to be influenced by growth in plasma and the changes were sometimes in the opposite direction to those seen with temperature upshifts: whereas expression of the invasin-encoding gene inv was significantly repressed during bacterial growth under both conditions, transcription of psaA (coding for the pH6 antigen) was promoted by temperature upshifts [6,20], but was one of the most strongly repressed in plasma. Interestingly, the impact of this medium on psaA transcription was not considered to be significant in Y. pestis and suggests that the pH6 antigen does not have the same importance in blood dissemination in the two species. In contrast to the latter two adhesins, transcriptional activation of yadA (harbored by the pYV plasmid and involved in adhesion) was found to be the highest of all the Y. pseudotuberculosis genes induced under plasma growth conditions. This observation is consistent with YadA's involvement in microbial resistance to complement [21,22]. Similarly, ompC whose product is believed to be targeted by lactoferricin [23], a bactericidal peptide derived from lactoferrin by enzymatic cleavage [24], is strongly repressed, whilst no significant modification was observed for the outer membrane-encoding genes ompA and ompC2.
Lastly, an essential determinant of bacterial virulence is the plasmid-encoded type III secretion system (TTSS) which performs intracellular delivery of a set of Yersinia outer proteins (Yops) that subvert the host's defenses [25]. Interestingly, Y. pseudotuberculosis growth in plasma induced the upregulation of 25 genes required for secretion, translocation and chaperoning of the Yop effector proteins in a similar fashion to that observed upon temperature upshift (Fig. 3). Furthermore, the apparently coordinated regulation of yadA and the TTSS-encoding genes by temperature and growth in plasma suggests the involvement of a common means of genetic control. YmoA (a chromatin-associated (histone-like) protein which is very similar in structure and function to the haemolysin expression modulating protein Hha from Escherichia coli) was shown to negatively influence YadA and Yop expression by favoring supercoiling of the pYV plasmid [26]. A two-fold reduction in ymoA transcription in plasma may be enough to contribute to the TTSS upregulation recorded in Y. pseudotuberculosis. Strikingly, this plasma-induced TTSS activation was not observed in Y. pestis, since only 3 out of the 25 genes mentioned above were found to be upregulated (in line with the statistically non-significant downregulation of ymoA); this raises the possibility that these two pathogenic Yersinia species may differ in their transcriptional regulation of pYV-harbored virulence genes.

Conclusion
Overall transcription profiling of Y. pseudotuberculosis grown in an environment mimicking the blood stage of the infectious process revealed gene regulations that could not be anticipated from the results of previously reported single-stimulus studies. Our findings thus provide insight into how a number of simultaneously sensed environmental cues may be taken into account by the bacterium in a hierarchical manner. Furthermore, comparison of our analyses with those previously performed in Y. pestis suggests that transcription of common critical virulence factors may be differently influenced (at least in part) by the plasma environment in these two species.

DNA macroarray construction
Pairs of specific oligonucleotide primers were designed with the Primer 3 software for each of the 3,951 Y. pseudotuberculosis IP32953 CDSs. In order to avoid cross-hybridization, the specificity of the PCR products relative to the complete genome sequence was tested with CAAT-box software [27]. Primers purchased from Eurogentec were chosen in order to specifically amplify a ≈ 400 to 500 base pair (bp) fragment of each open reading frame (ORF), with a melting temperature of 51 to 60°C. Amplification reactions were performed in 96-well plates (Perkin-Elmer) in a 100 μl reaction volume containing 100 ng of Y. pseudotuberculosis IP32953 DNA, DNA polymerase (Dynazyme, New England Biolabs), 10 μM of each primer and 2 mM dNTPs (Perkin-Elmer). Reactions were cycled Medium-and temperature-dependent differential expression of genes harbored by the Y. pseudotuberculosis virulence plasmid pYV Figure 3 Medium-and temperature-dependent differential expression of genes harbored by the Y. pseudotuberculosis virulence plasmid pYV. Significant (p < 0.05) upshifts (yellow to red scale) or downshifts (blue scale) in individual gene transcription levels when bacteria were grown in human plasma versus LB (triangles) and/or at 37°C versus 28°C (squares) are indicated by the color scale bar. Only genes spotted on the macroarray (56 out of 99 pYV-borne genes) are shown and those encoding the secretion apparatus and Yop effectors are represented by grey and black boxes, respectively. Mean fold changes in transcription and p-values are indicated in Table 3.
45 times (94°C for 30 s; 60°C for 30 s; 72°C for 60 s) with a final cycle of 72°C for 7 min in a thermocycler. Each PCR product was checked by agarose gel electrophoresis and when DNA amplification was unsuccessful, PCR was repeated with another primer set. Overall, 3,951 of the 3,994 CDSs (98%) identified in the Y. pseudotuberculosis IP32953 genome were successfully amplified under our experimental conditions. ORF-specific PCR products, luciferase DNA (10 to 100 ng) and total genomic DNA from strain IP32953 were spotted onto 22 × 7-cm nylon membranes (Genetix) using a Qpix robot (Genetix). Immediately following spot deposition, membranes were immersed for 15 min in 0.5 M NaOH and 1.5 M NaCl, washed three times with distilled water and stored at -20°C until use. To ensure that DNA samples were successfully deposited on the membranes, 33 P-labeled genomic DNA was hybridized to the macroarray before transcriptome analysis.


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Sperm morphology and chromatin integrity in Swedish warmblood stallions and their relationship to pregnancy rates

Background Artificial insemination is not as widely used in horses as in other domestic species, such as dairy cattle and pigs, partly because of the wide variation in sperm quality between stallion ejaculates and partly due to decreased fertility following the use of cooled transported spermatozoa. Furthermore, predictive tests for sperm fertilising ability are lacking. The objective of the present study was to assess sperm morphology and chromatin integrity in ejaculates obtained from 11 warmblood breeding stallions in Sweden, and to evaluate the relationship of these parameters to pregnancy rates to investigate the possibility of using these tests predictively. Methods Aliquots from fortyone ejaculates, obtained as part of the normal semen collection schedule at the Swedish National Stud, were used for morphological analysis by light microscopy, whereas thirtyseven were used for chromatin analysis (SCSA) by flow cytometry. The outcome of inseminations using these ejaculates was made available later in the same year. Results Ranges for the different parameters were as follows; normal morphology, 27–79.5%; DNA-fragmentation index (DFI), 4.8–19.0%; standard deviation of DNA fragmentation index (SD_DFI) 41.5–98.9, and mean of DNA fragmentation index (mean_DFI), 267.7–319.5. There was considerable variation among stallions, which was statistically significant for all these parameters except for mean_DFI (P < 0.001, P < 0.01, P < 0.001 and P < 0.2 respectively). There was a negative relationship between normal morphology and DFI (P < 0.05), between normal morphology and SD_DFI (P < 0.001), and between normal morphology and mean_DFI (P < 0.05). For specific defects, there was a direct relationship between the incidence of pear-shaped sperm heads and DFI (P < 0.05), and also nuclear pouches and DFI (P < 0.001), indicating that either morphological analysis or chromatin analysis was able to identify abnormalities in spermiogenesis that could compromise DNA-integrity. A positive relationship was found between normal morphology and pregnancy rate following insemination (r = 0.789; P < 0.01) and a negative relationship existed between DFI and pregnancy rate (r = -0.63; P < 0.05). Sperm motility, assessed subjectively, was not related to conception rate. Conclusion Either or both of the parameters, sperm morphology and sperm chromatin integrity, seem to be useful in predicting the fertilising ability of stallion ejaculates, particularly in determining cases of sub-fertility.

Background
The number of artificial inseminations (AI) carried out in mares has increased considerably over recent years but, at around one million inseminations worldwide annually, use of this technique lags far behind that seen in other domestic animals, particularly those species used for food production. More than 100 million inseminations in cattle and more than 250 million inseminations in turkeys are carried out annually. Some of the limiting factors in the use of AI for mares are believed to be as follows: (i) highly variable quality between stallions and between ejaculates [1]; (ii) a decline in conception rate associated with the increased use of cooled transported spermatozoa rather than AI with fresh spermatozoa immediately after semen collection; (iii) lack of suitable on-site tests for sperm quality assessment [2]. Variation in stallion semen quality has been attributed to the selection of breeding sires based only on competitive performance [1].
Sperm quality, being multi-factorial, is difficult to define, and the different factors vary in significance between different species. Currently most studs rely on a subjective assessment of sperm motility as the sole determinant of sperm quality in insemination doses, although its correlation with fertility tends to be low [3]. Recently it has been suggested that a battery of physical and functional in vitro tests should be carried out on spermatozoa in an attempt to predict their likely fertilising capacity [1,2]. Such tests include sperm morphology [3][4][5] and chromatin integrity [6]. Disturbances in spermatogenesis give rise to sperm morphological abnormalities, although a wide range of sperm abnormalities may be acceptable for normal stallions if the proportion of spermatozoa with normal morphology is above 50% [7,8]. Specific morphological abnormalities are related to male sub-fertility [4].
Studies with human in vitro fertilization (IVF) have identified chromatin integrity as being of considerable importance in determining the outcome of assisted reproduction. A study in fertile stallions, in which the Sperm Chromatin Structure Assay (SCSA) was used to evaluate sperm chromatin [6], revealed a negative correlation between fertility and chromatin damage in sperm doses for AI [9,10]. The SCSA assay uses acridine orange to stain the sperm DNA, which is then analysed by flow cytometry to evaluate the ratio of single-stranded (abnormal) and double-stranded (normal) DNA present in individual spermatozoa.
The objective of the study reported here was to investigate whether sperm morphology and chromatin integrity can be used predictively in evaluating the quality of stallion spermatozoa for AI, using aliquots of ejaculates collected from stallions on a commercial stud in Sweden.

Animals and husbandry
Warmblood stallions of breeding age (7 to 23 years old) were housed under standard husbandry conditions at Flyinge AB, Flyinge, Sweden. Semen was collected up to three times a week during the normal breeding season (April to August in Sweden) as part of a commercial enterprise. Ethical permission for the study was granted by the Ethics Committee on Animal Experimentation, Uppsala, Sweden (C107/6): all experiments were carried out according to recognized international guidelines. The ejaculates used in this study were collected over a 15 day period in June 2006, with aliquots (6 mL) of the extended ejaculates being made available for the experiments. The remainder of the ejaculates was processed and supplied to customers for AI according to the usual procedures at the stud.
The ejaculates were extended 1:1 (v/v) with either Kenney's extender or Nørlund medium (see below) at 37°C. The Kenney's extender was used for stallions F, Y, T, A, R and K, while Nørlund medium was used for stallions H and W. Note: the stud personnel had recognized low pregnancy rates following AI with cooled, transported sperm doses for these two stallions from previous years, and were testing a different extender for these stallions in an attempt to rectify this situation.
Nørlund medium: an egg yolk-containing extender, purchased from Nørlunds Equine Hospital, Rue de Lund, 8653 Them, Denmark. (Note: The manufacturer is not prepared to reveal the ingredients contained in this extender).

Sperm concentration
The concentration of spermatozoa in the original ejaculate was measured using a Spermacue photometer (Minitüb, Tiefenbach, Germany).

Subjective estimation of motility
Subjective motility assessment was performed on aliquots (5.0 µL) of the extended ejaculate, using phase contrast light microscopy (x100) on a heated microscope stage (38°C), immediately after adding the extender and once daily until the motility had dropped to 20%. Sperm samples were stored at 4°C.

Sperm Morphology
Smears of extended ejaculates were made on clean glass slides. In addition, a few drops of each sperm suspension were added to buffered formaldehyde [11]. Two hundred spermatozoa in these wet smears were evaluated by experienced laboratory assistants using phase contrast microscopy at 1,000× magnification with an immersion objective lens. Spermatozoa displaying morphological abnormalities (proximal cytoplasmic droplet, loose heads, abnormal acrosomes, nuclear pouches, abnormal mid-piece, simple coiled tails, tail coiled under the head, and double coiled tails) were identified. For sperm head morphology, 500 spermatozoa were examined in Williams-stained slides using a light microscope at 1,000× magnification, and the numbers of sperm head abnormalities (pear shape, narrow-based, abnormal contour, lack of development, narrowness, abaxial implantation, and abnormal loose heads) were identified. The staining method used was the modification of William's method [12] described by Lagerlöf [13]. All morphological abnormalities in the spermatozoa were recorded (i.e. both head and tail abnormalities may be present in one spermatozoa). The mean proportion of morphologically normal spermatozoa was estimated as the remaining proportion left from total abnormal spermatozoa counted both in wet smear and Williams stain (100 total abnormalities). Note: spermatozoa with a distal droplet but otherwise normal were counted as normal.

Sperm chromatin structure assay
Abnormal chromatin structure was defined as the susceptibility of sperm DNA to undergo acid-induced denaturation in situ. Following exposure of the prepared DNA to acridine orange (AO), the degree of chromatin integrity was analysed by flow cytometric measurement (FCM) of the metachromatic shift from green (stable, doublestranded DNA) to red (denatured, single-stranded DNA) AO fluorescence [6]. This shift was expressed as the ratio of red to total (i.e. red and green) fluorescence intensity. In the SCSA, this ratio was calculated for each spermatozoon within a sample and the results were expressed as the ratio of single stranded to double stranded DNA [DFI (DNA fragmentation index, %), its standard deviation (SD_DFI) and the measure of single stranded DNA (mean_DFI)] [14].
In the present study, the procedure originally developed by Evenson et al. [6] and later described in detail by Januskauskas et al. [15,16] was followed. Aliquots of extended semen or centrifuged sperm preparations were mixed with TNE buffer (0.15 M NaCL, 0.01 M Tris-HCL, 1 mM EDTA (ethylenediaminetetra-acetic acid), pH 7.4) to a final sperm concentration of approximately 2 × 10 6 cells/mL. An aliquot of 0.2 mL was immediately frozen in liquid nitrogen vapour (LN 2 ) and then transferred to an ultra-cold freezer (-80°C), where it was stored until further processing and FCM analysis. This procedure has been used for many years in our laboratory without any evidence of deterioration of the DNA samples during processing or storage. Samples of stallion spermatozoa with known high chromatin instability were processed in the same manner and served as controls for the procedure.
Just prior to analysis, the samples were thawed on crushed ice. The thawed, TNE-extended spermatozoa were subjected to partial DNA denaturation in situ (by mixing with 0.4 mL of a low pH detergent solution containing 0.17% Triton X-100, 0.15 M NaCl and 0.08 N HCl, pH 1.2), followed 30 seconds later by staining with 1.2 mL of AO (6 µg/ml in 0.1 M citric acid, 0.2 M Na 2 HPO 4, 1 mM EDTA, 0.15 M NaCL, pH 6.0). The stained samples were analysed within 3-5 minutes of AO staining. Samples of stallion spermatozoa with known high chromatin instability were processed in the same manner and served as controls for the procedure (Reference sample for SCSA): these reference samples were previously analysed samples of stallion spermatozoa found to have high (30-50%) DFI-values. When samples having such high DFI values are encountered, the remainder of the sample is aliquoted and re-frozen for subsequent use as a reference sample.
Measurements were made on a FACStar Plus flow cytometer (Becton Dickinson, San José, CA, USA) equipped with standard optics. Acridine orange was excited with an Ar (argon) ion laser (Innova 90; Coherent, Santa Clara, CA, USA) at 488 nm, running at 200 mW. As previously mentioned, in association with double-stranded DNA, AO fluoresces green (530± 30 nm, as detected using the FL 1 detector), but in the presence of single-stranded DNA the fluorescence is red (>630 nm, as detected with the FL 3 detector). The fluorescence stability of the flow cytometer was monitored daily using standard beads (Fluoresbrite plain YG 1.0 µM; Polysciences Inc., Warrington, PA, USA). Equivalent instrument settings were used for all samples. From each sample a total of 10,000 events were measured at a flow rate of ~200 cells/s. Data collection was carried out using CellQuest, version 3.3 (Becton Dickinson, San José, CA, USA). Further calculations were performed using FCS Express version 2 (De Novo Software, Thornhill, Ontario, Canada).

Pregnancy rate
The mean pregnancy rate (PR) for the ejaculates used in this study was available later in the same year. For the purposes of this study, PR was taken to be: PR = (no. mares pregnant/no. mares inseminated) × 100 (%) Note: the stud where the stallions were kept was a commercial enterprise selling insemination doses to the owners of mares at various locations around Sweden.
Therefore, no details of the insemination technique, oestrus and pregnancy detection are available.

Statistics
Analysis of variance [17] was performed on mean values of the following parameters: the proportion of spermatozoa with normal morphology, DFI, SD_DFI and mean_DFI values for each stallion. Differences were considered to be statistically significant if P < 0.05. A regression analysis [17] was carried out on the following: normal morphology with PR; parameters of SCSA with PR, with the incidence of specific morphological defects, with sperm survival and with age of stallion. Again, values of P < 0.05 were considered significant.

Results
The characteristics of the 41 ejaculates made available for this study are shown in Table 1. There was considerable variation among stallions in terms of sperm concentration and motility in the ejaculates: however, only sperm concentration between stallions differed significantly (P = 0.03). Differences in sperm concentration among ejaculates for each stallion were not significant.
The proportion of morphologically normal spermatozoa in the extended ejaculates varied between stallions (P < 0.001) ( Table 1). The variation among stallions was highly significant but the variation between ejaculates (within stallions) was not significant. Furthermore, there was considerable variation between stallions in the prevalence of individual abnormalities although differences within stallions were not significantly different except for abnormal acrosomes (P < 0.05). Of the morphological abnormalities observed, the most prevalent in the extended semen were proximal cytoplasmic droplets (7-27%), pear-shaped heads (3.6-7%), heads with narrow bases (1.5-5%) and narrow heads (1-3%).
Regarding assessment of chromatin integrity (performed on 37 ejaculates), the mean (± SD) values for DFI, SD_DFI and mean_DFI for each of the 11 stallions, and the overall mean (± SD) for all ejaculates are shown in Table 2. Variation among stallions was significant for DFI (P < 0.01) and for SD_DFI (P < 0.001) but not for mean_DFI (P < 0.2). However, variation between ejaculates for individual stallions was significant only for mean_DFI (P < 0.05).
Correlation analysis was performed for DFI, SD_DFI, and mean DFI and (i) normal morphology, (ii) total sperm head morphology, and (ii) the incidence of specific morphological defects. There was a negative relationship between normal morphology and DFI (r = -0.34, P <0.05), and between normal morphology and SD_DFI, (r = -0.43, P < 0.01). For specific abnormalities, there was a direct relationship between the incidence of pear-shaped heads and DFI (r = 0.34; P < 0.05), nuclear pouches and DFI (r = 0.47; P < 0.01), mid-piece defect and DFI (P < 0.01); between detached heads and SD_DFI (P < 0.01); and between detached heads and mean_DFI (P < 0.05). There was no statistically significant relationship between the other morphological defects and either DFI, SD_DFI or mean_DFI, nor between DFI and total sperm head abnormalities, nor between the age of stallion or length of sperm survival (defined for the purposes of this study as the number of days sperm motility was >20%) and the SCSA parameters.
The mean PR of these insemination doses, obtained retrospectively, is shown in Table 3. There was a positive relationship between % normal morphology and PR Notes. Concentration was determined by Spermacue. Differences between stallions were significant for concentration (P = 0.03) and normal morphology (P < 0.001) but not for motility (P0 0.1). Differences between ejaculates for each stallion were not significant for concentration, motility or normal morphology.
following insemination (r = 0.789; P < 0.01) (Figure 1), and a negative relationship between DFI and PR (r = -0.63; P <0.05), with higher values of DFI being associated with a lower number of pregnancies following artificial insemination ( Figure 2).

Discussion
Values for concentration and sperm motility were within the normal range for this species [18]. Considerable variation in these parameters between stallions has also been observed by others [1,19], although in this study there was no difference between ejaculates from each stallion collected over a short time period (15 days) at the height of the breeding season. Seasonal variation has been reported previously among stallion ejaculates [19].
The observations of a positive relationship between normal morphology and PR together with a negative relationship between DFI and PR are in accordance with other findings [4,9,10]. The relationship seen here between normal morphology and intact chromatin is also in accordance with previous observations. The link between the presence of certain morphological defects and chromatin damage is interesting and warrants further study, particularly those defects related to the condensation of the chromatin of the round spermatids during spermiogenesis. As Relationship between DNA fragmentation rate (DFI) and pregnancy rate (%) following artificial insemination with cooled, transported stallion spermatozoa from the ejaculates tested in this study (n = 37) Figure 2 Relationship between DNA fragmentation rate (DFI) and pregnancy rate (%) following artificial insemination with cooled, transported stallion spermatozoa from the ejaculates tested in this study (n = 37). The line of best fit for the data points is shown. Note: Statistics r = -0.63; P < 0.05).  Notes: DFI gives a measure of the proportion of spermatozoa with damaged chromatin; SD_DFI is the standard deviation of DFI; mean_DFI is a measure of single stranded DNA. Variation between stallions is statistically significant for DFI and SD_DFI (P < 0.001), variation between ejaculates is significant only for mean_DFI (P < 0.05).
Relationship between normal morphology (%) and pregnancy rate (%) following artificial insemination with cooled, trans-ported stallion spermatozoa derived from the ejaculates tested in this study (n = 41) Figure 1 Relationship between normal morphology (%) and pregnancy rate (%) following artificial insemination with cooled, transported stallion spermatozoa derived from the ejaculates tested in this study (n = 41). The line of best fit for the data points is shown. Note: Statistics r = 0.789; P < 0.01. such, there was a clear relationship between the presence of nuclear pouches and damaged chromatin. Therefore, the presence of pear-shaped heads and/or nuclear pouches in stallion spermatozoa could be useful as markers for suspected chromatin damage in this species, when the resources for the SCSA are lacking (for instance when no flow cytometer is available). A significant relationship between DFI and the occurrence of pear-shaped heads has been reported previously for boar spermatozoa, using the Sperm Chromatin Dispersion test (SCD) [20]. A strong relationship between the incidence of midpiece defects and damaged chromatin, as well as detached heads and SD_DFI, was observed in this study, although we do not have any logical, biological explanation for these relationships.
It is not unusual to encounter a number of abnormal sperm forms in any ejaculate; only when present in large numbers are morphological abnormalities associated with impaired fertility in the stallion [21]. The situation may be somewhat different in other species: for example, statistically significant correlations have been found between abnormal heads, nuclear pouches and proximal cytoplasmic droplets and fertility (56-day non-return rate) in bull ejaculates [22]. Bent tails and single coiled tails may be caused by derangement in the secretion of accessory fluids: bent tails may also result from pH and osmotic tension of the semen extender, while single coiled tails may be a preparation artifact. It has been suggested that causes for concern in the differential spermiogram would include less than 30% morphologically normal spermatozoa, more than 10% immature germ cells, more than 30% abnormal sperm heads and/or midpiece defects, or more than 25% spermatozoa with proximal cytoplasmic droplets [4]. Such levels are usually associated with decreased fertility.
One of the stallions in the study reported here, stallion H, had a mean of only 31.2% morphologically normal spermatozoa in the three ejaculates examined, very close to the 30% cut-off point mentioned above [4]; of the abnormalities observed, 25% were midpiece defects. Insemination of the three ejaculates included in this study produced only two pregnancies in 13 mares, well below the pregnancy rates of ejaculates from the remaining stallions. Another stallion (W) had a mean of 27% proximal droplets in three ejaculates. This high incidence might be expected to affect fertility, although two out of three mares inseminated with the ejaculates analysed in this study became pregnant. The other stallions had 65-75% morphologically normal spermatozoa with a low incidence of individual abnormalities: fertility rates varied from 50 to 78%.
The chromatin quality of all the ejaculates used in this study would have been considered either excellent or good according to Love's classification scheme [10]. Love, furthermore, suggested that DFI levels above 27% were linked to reduced fertility [10].
It should be noted that the mean pregnancy rate used here is only a rough guide to the fertility of the ejaculates since it does not take into account such extraneous variables as female factors and inseminator differences. Furthermore, some mares were inseminated several times during each oestrous using different ejaculates from the same stallion, making it impossible to identify the source ejaculate of the fertilising spermatozoon. These factors are a recognized difficulty in reporting "fertility" in horses [23].
The amount of seminal plasma included in extended semen doses for AI is thought to influence DNA quality markedly, with detrimental effects becoming apparent when semen samples are cooled and stored [10]. Stallion ejaculates to be transported for artificial insemination are extended with a suitable medium, e.g. Kenney's extender, to give an insemination dose of one billion spermatozoa in 20 mL, and consequently there is variable "dilution" of the seminal plasma between different ejaculates. This differential dilution of seminal plasma and, consequently, the exposure of spermatozoa to its potentially chromatindamaging effects, may help to explain why there is so much apparent variation in the fertility of stallion ejaculates. Preliminary observations carried out on five ejaculates, at the beginning of the study reported here, showed a highly variable DFI result when extended semen was stored, cooled, for 24 hours in transport boxes: for two ejaculates, values of DFI rose to > 25%, whereas the remaining three ejaculates showed much smaller increase in DFI of around 2-5%. It is intended to look at chromatin integrity during storage of sperm samples at different temperatures in a later experiment.


extfieldsofstudy:
Medicine
Computer Science
Biology

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miR2Disease: a manually curated database for microRNA deregulation in human disease

‘miR2Disease’, a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA–disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA–disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA–disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA–disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.

INTRODUCTION
MicroRNAs are a class of endogenous single-stranded small noncoding RNAs that negatively regulate gene expression (1). MicroRNAs control mRNA degradation and/or translation inhibition through binding to complementary sites in the 3 0 -untranslated regions of target genes (2). In the past decade, hundreds of microRNAs have been identified in mammalian cells (3,4). Evidence suggests that microRNAs play critical roles in multiple biological processes, including cell cycle control, cell growth and differentiation, apoptosis, embryo development and so on (5)(6)(7)(8).
In recent years, dozens of microRNA-related database systems have been developed. miRBase (9), miRGator (10) and miRGen (11) aim at providing complete repositories for microRNA annotation and nomenclature. TarBase (12), microRNAMap 2.0 (13) and microRNA.org (14) collect experimentally validated and/or computationally predicted microRNA-target relationships; microRNA.org also provides a collection of microRNA expression profiles in different tissues. Such database systems offer great resources in investigating the function of microRNA in gene regulation. In addition, several computational algorithms and web-based programs have been developed to computationally predict target genes/sites of microRNAs, such as TargetScan (15), PicTar (16), RNAhybrid (17) and PITA (18).
Accumulating evidence indicates that microRNAs play crucial roles in human disease development, progression, prognosis, diagnosis and evaluation of treatment response (19)(20)(21)(22)(23)(24). Genome-wide association studies demonstrated that many human microRNA genes locate at genomic regions linked to cancer (25,26). Moreover, a recent study found that the absolute expression levels of many microRNAs were reduced significantly in tumors (27). The same study also revealed that using the expression levels of 217 microRNAs were more effective in cancer classification than mRNA microarrays containing more than 16 000 protein-coding genes (27). All these evidences support the strong necessities in understanding the function of microRNA in disease development.
In addition to these genome-wide studies, correlations between single microRNA deregulation and occurrences of human disease have been reported. Cimmino et al. (28) demonstrated that both miR-15a and miR-16-1 negatively regulate BCL2 at a post-transcriptional level, which induces apoptosis in a leukemic cell line model. Yang et al. (29) found that miR-1 overexpression slowed cardiac conduction and depolarized the cytoplasmic membrane by post-transcriptionally repressing KCNJ2 and GJA1, which accounted at least in part for its arrhythmogenic potential. Hitherto, over 100 review papers have been published in summarizing the relationship between microRNA deregulation and dozens of diseases (30)(31)(32)(33)(34)(35)(36). Despite these developments, detailed information on microRNA-disease relationships are scattered in literatures and there is no online repository for these microRNA-disease relationships. Therefore, we develop a manually curated database entitled 'miR2Disease' (http://www.miR2Disease.org), which provides a comprehensive resource of microRNA deregulation in various human diseases.

DATA COLLECTION AND DATABASE CONTENT
Initial entries describing the relationships between microRNA deregulation and occurrences of human disease are collected manually. We searched the PubMed database (37) with a list of keywords, such as 'microRNA disease', 'miRNA disease', 'microRNA cancer', 'miRNA cancer', etc. In the current release of miR2Disease, more than 600 literatures were reviewed, and 1939 curated relationships between 299 human microRNAs and 94 human diseases were documented. In the miR2Disease system, the disease terminologies were organized based upon a controlled medical vocabulary (Disease Ontology: http://diseaseontology.source forge.net/), which utilizes the Unified Medical Language System (UMLS) as source vocabulary (38). Such organization provides substantial advantage in terms of search and analysis. Each entry in the database contains detailed information on a microRNA-disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA-disease relationship, an expression pattern of microRNA (upregulated or downregulated) in the disease state, the detection method used to derive microRNA expression pattern (microarray, northern blot, qRT-PCR, etc.), the experimentally validated target gene(s) extracted from the corresponding references and directly derived from TarBase and a literature reference. miR2Disease provides a user-friendly interface for an easy query of each entry by microRNA ID, disease name or experimentally validated target genes. In addition, convenient links are provided to other microRNA databases, including microRNA sequence and annotation in miRBase, experimentally validated microRNA target genes in TarBase and computationally predicted microRNA target genes in TargetScan, miRanda and PicTar. A hyper link to the literature reference in the NCBI PubMed database (37) together with an official PubMed ID and complete citation are also provided. In miR2Disease, around one-seventh of the microRNAdisease relationships represent the pathogenic roles of deregulated microRNA in human disease.

USER INTERFACE
Search page miR2Disease provides a search engine to query detailed information on each microRNA-disease relationship documented in the database. Users can query the database through microRNA ID, disease name or target genes.
miR2Disease offers a fuzzy search function. Combining with controlled disease vocabulary (disease ontology), fuzzy search functionality allows users to retrieve meaningful microRNA-disease relationship information without knowing the exact disease name documented in the database. Once certain disease name is received as a query term, the system first searches all the disease terminologies that contain the query words. The matching disease terminology will be listed in the context of a disease tree, which contains its ancestor nodes as well as all of its subcategories. In the matching disease tree, the categories that contain query terminologies are highlighted as bold, and a hyperlink is created for every category that contains documented microRNA-disease relationships. Through these hyperlinks, users can retrieve all the microRNA entries that are related to the selected disease terminology. In the search results page, clicking the 'more . . .' link at the end of the entry will lead to detail information of this relationship ( Figure 1). Similar to search by disease terminology, the nomenclatures of microRNAs could be complicated and confusing. It is not unusual that the original publication did not provide enough information on the exact microRNA in a large microRNA family. For instance, some publications simply report let-7 is related to adenoma, while others conclude that the expression level of let-7a-3 is downregulated in breast cancer. Therefore, through the fuzzy search function, miR2Diesease allows queries of microRNAs without knowing their exact identification numbers. Under the circumstances that one microRNA query results in multiple IDs, one can further select the microRNAs of the greatest interests. Similarly, Figure 1 provides a snapshot of the query workflow of 'search by miRNA ID'. In the miR2Disease system, the concept of target genes can be classified into three categories: the target genes reported in the original reference, the target genes documented in the TarBase system (experimentally validated targets) and the predicted targets from computational tools (Miranda, TargetScan or PicTar). The 'search' by 'target genes' functionality allows users to search microRNA-disease relationships from the target genes of the first two types (reported targets and experimentally validated targets). Searching through the computationally predicted targets will be included in the future release.
The causal relationship between disease and microRNA is documented and listed in both the searching result page and the detailed relationship page (Figure 1). In addition, the system also allows users to filter the search results by only selecting causal relationships.
Submit page miR2Disease provides a submission page that allows other researchers to submit important microRNA-disease relationships that are not documented. Once approved by the submission review committee, the submitted record will be included in the database, and made available to the public in the coming release. miR2Disease will be updated monthly.

DISCUSSION
Emerging evidence suggests that specific temporal and spatial microRNA expression is required for normal cellular development and differentiation, whereas many human diseases are associated with aberrant microRNA expression patterns. In order to provide a central resource for the biologists who study the relationship between microRNA and human disease, we develop miR2Disease, a database system aiming at providing a comprehensive resource of microRNA deregulation in various human diseases. miR2Disease not only offers an easy-to-use web interface to query the detailed information on microRNA-disease relationships documented in the database, but it also provides a submission page that allows other researchers to contribute to the data contents. Figure 2A demonstrates the histogram of the number of diseases associated with the microRNAs archived in the database. Eighty-seven microRNAs (29.1%) are deregulated in only one disease; while 177 microRNAs (59.2%) demonstrate deregulation in no more than three diseases. The most prevalent microRNA appeared in the database is hsa-miR-21, which is deregulated in 59 documented diseases (over half of all the disease types). Five additional microRNAs are deregulated in over 25 types of diseases; they are: hsa-miR-155, hsa-miR-221, hsa-let-7a, hsa-miR-223 and hsa-miR-222, which are related to 45, 37, 28, 25 and 25 diseases, respectively. The histogram of the number of deregulated microRNAs associated with individual disease is shown in Figure 2B. No more than three microRNA deregulations were documented for 37 diseases (39.4%), 21 of which contain only one record. On the contrary, 13 diseases are related to more than 50 deregulated microRNAs. Hepatocellular carcinoma, prostate carcinoma and colorectal carcinoma top this list with 129, 98 and 87 deregulated microRNAs associated. One should notice that these numbers are highly dependent on the experimental approaches being used in the original publication. For instance, investigations using microarray technology usually observe more deregulated microRNAs than other low-throughput studies.
It remains to be seen whether the deregulation of microRNAs is a cause or consequence of the disease state. Around 1/7 of the microRNA-disease relationships in miR2Disease indicate the pathogenic role of microRNA deregulation in various diseases, including cancer, metabolic disease and cardiovascular disease. For instance, Ma et al. (39) reported that highly expressed miR-10b initiates tumor invasion and metastasis in breast cancer through translational inhibition of HOXD10, and eventually increases expression of RHOC, a pro-metastatic gene. Huang et al. (40) found that significant upregulation of miR-373 and miR-520c stimulates breast cancer cell migration and invasion by the suppression of CD44. Meng et al. (41) identified that the oncogene mitogenactivated protein kinase kinase kinase 8 (MAP3K8) is a target of miR-370, whose reduced expression elevates the MAP3K8 level and therefore contributes to tumor growth in cholangiocarcinoma. It has been reported that miR-375 controls insulin secretion by regulating the expression of myotrophin. Upregulation of miR-375 led to an enhanced inhibition of insulin release (42). It is also demonstrated that downregulation of miR-1 and miR-133 contributes to re-expression of HCN2/HCN4 and the electrical remodeling process in hypertrophic hearts (43).
Information in the miR2Disease can be used to describe the relationship among multiple diseases. To this end, we create a bipartite network that describes the causal relationship between 85 microRNAs and 32 cancer-related diseases (Figure 3). Within this network, lung carcinoma, breast cancer and ovarian cancer demonstrate the highest connectivity by affiliating with 25, 23 and 18 causal microRNAs, respectively. For microRNAs, hsa-let-7a deregulation is the causal effect for nine types of cancers, while hsa-miR-21, hsa-miR-124a, hsa-let-7c, hsa-miR-145, hsa-miR-16 and hsa-miR-221, each associate with five types of cancers or more (solid circles in the network). Using miR2Disease database, one can create this type of bipartite network by searching for the diseases or microRNAs of interest.
miR2Disease documented several potential mechanisms that cause the microRNA deregulation in various human diseases. First, microRNAs are located in the diseasecausing genomic loci, including minimal regions of loss of heterozygosity, minimal amplicon, or breakpoint cluster regions (19,25). For instance, miR-15 and miR-16 are located in chromosome 13q14, a region that is deleted in more than half of B-cell chronic lymphocytic leukemia (CLL)s (B-CLL); consequently, both genes are deleted or downregulated in the majority (68%) of CLL cases (44). In contrast, the miR-17-92 polycistron is located in a genomic region that is amplified in human B-cell lymphomas, and therefore are overexpressed (45). Second, microRNA deregulation is caused by abnormal epigenetic patterns, including abnormal DNA methylation and histone-modification patterns (46). For instance, in normal conditions, the putative promoter region of let-7a-3 gene is heavily methylated in normal human tissues, Figure 3. A bipartite network demonstrating the causal relationship between deregulated microRNAs and variety of cancers. Circles and rectangles correspond to microRNAs and diseases, respectively. An edge is placed between a microRNA and a disease if the microRNA deregulation plays causative role in pathogenesis of the disease. Solid circles indicate master microRNAs that potentially cause no less than five types of cancers. but hypo-methylated in lung adenocarcinoma. Such promoter demethylation induced reactivation of the let-7a-3 with an oncogenic function and increased proliferation in lung cancer cell lines (47). In addition, aberrant hypermethylation leads to miR-9-1 inactivation in human breast cancer (48). Third, microRNA deregulation may be caused by abnormalities of the enzymes that are involved in microRNA biogenesis. For example, Otsuka et al. (49) unambiguously showed that miR-24 and miR-93 could target viral large protein (L protein) and phosphoprotein (P protein) genes. In Dicer1-deficient cells, a lack of host miR-24 and miR-93 was responsible for increased VSV replication. In the miR2Disease system, such information is documented in the 'analysis' session (a link through the home page).
In addition to the mechanisms described above, miR2Disease also contains entries where disease-causing genetic variations affect cellular functions through the disruption of microRNA target selection. For instance, loss-of-interaction variations in let-7:Hmga2 (50), miR-148a:HLA-G (51) and miR-433:FGF20 (52), are involved in myoma, asthma and Parkinson's disease, respectively. Chen et al. (53) elucidated that CCND1 mRNA is normally under the regulation of miR-16-1, and that truncated CCND1 mRNA escapes this regulation through deletion of the microRNA-binding sites in mantle cell lymphoma. In summary, loss-of-interaction variations in microRNA genes or the binding site(s) of microRNA represent a newly described pathogenic mechanism of disease.
Overall, miR2Disease provides a comprehensive resource of microRNA deregulation in human disease. We believe that miR2Disease will be of particular interest to the life science community and facilitates the biologists to unravel the role of microRNA in the pathogenesis of human disease.

FUTURE DEVELOPMENT
As stated in the 'data collection and database content' session, the microRNA-disease relationships documented in the current release were collected through searching the PubMed database with a list of keywords, such as 'microRNA disease', 'miRNA disease', 'microRNA cancer', 'miRNA cancer', etc. Although we collected 2000 relationships from over 600 literatures, such mechanism suffers from the lack of comprehensiveness and systematicness. We plan to adopt two strategies to improve our data collection. First, we will use text-mining tools to help us prescreen PubMed abstracts that potentially describe the microRNA-disease relationships. Second, we will specifically target MeSH (Medical Subject Headings) vocabularies that are created and maintained at the National Library of Medicine. These strategies will no doubt increase the data comprehensiveness, and will impact the database records in the releases to come.

DISCLAIMER
Not all relationships of microRNA in disease are equally valid. Some studies demonstrated more creditable microRNA-disease relationships than others.


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Cloning, Expression, Purification and Crystallization of the PR Domain of Human Retinoblastoma Protein-Binding Zinc Finger Protein 1 (RIZ1)

Through alternative promoter usage, human retinoblastoma protein-interacting zinc finger gene RIZ encodes two different protein products, RIZ1 and RIZ2, which have been identified to be a tumor suppressor and a proto-oncoprotein, respectively. Structurally, the two protein products share the same amino acid sequences except that RIZ2 lacks an N-terminal PR domain with methyltransferase activity. Previous studies have shown that over-expression of RIZ2 is usually associated with depressed RIZ1 expression in different human cancers. It is generally believed that RIZ1 and RIZ2 regulate normal cell division and function using a “Yin-Yang” fashion and the PR domain is responsible for the tumor suppressing activity of RIZ1. In order to better understand the biological functions of the PR domain by determining its three-dimensional crystal structure, we expressed, purified and crystallized a construct of the PR domain (amino acid residues 13–190) in this study. The maximum size of the needle-shaped crystals was approximately 0.20 × 0.01 × 0.01 mm.

RIZ1 and RIZ2 have identical amino acid sequences except that RIZ2 lacks the N-terminal PR domain region (200 amino acids, a subclass of zinc finger proteins present in PRDI-BF1/Blimp-1 and RIZ1) [10,12,13]. Therefore, the PR domain is likely responsible for the tumor suppressing activity of RIZ1. In vitro studies showed that the PR domain interacts with a PR binding motif (PRB) located in the C-terminal region of both RIZ1 and RIZ2 [11,20,21]. This interaction might play an important role in the "Yin-Yang" regulation between RIZ1 and RIZ2. Structurally related to the SET domain of the chromatin-associated proteins involved in gene expression, the PR domain is a C2-H2 type of zinc finger protein possessing histone H3K9 methyltransferase activity [14,22]. Currently, more than 20 PR domains and 30 SET domains are identified in human [22,23]. Amino acid sequence identity is about 45% among the PR domains, 50% among the SET domains, and 20% between the PR and SET domains [23]. In 2005, Derunes et al. first reported the crystallization of a construct (amino acid residues 1-161) of the PR domain [22]. However, the crystallization conditions were not released and the crystal structure of the construct has not yet been reported. This research group however reported NMR structures of the same construct (re-classified as a SET domain) earlier this year [24]. Despite the availability of the NMR structures, our current crystallographic investigation on the PR domain was continued because our PR domain construct (amino acid residues  is much longer in the Cterminus and valuable information may be deduced for residues 161 to 190 from the crystal structure. In addition, comparison between the crystal structure and the NMR structures of the PR domain might reveal some key features that are crucial for its in vivo biological functions.

Cloning and expression of DNA fragment encoding the PR domain
As described below in the Experimental Section, a DNA fragment encoding the PR domain of RIZ1 (amino acid residues  was cloned by the polymerase chain reaction (PCR) using a plasmid DNA containing the full-length RIZ gene as the template in order to characterize the PR domain by the X-ray protein crystallography. The PCR product was confirmed to have 534 bp ( Figure 1) and share 100% sequence identity with gene fragment encoding the designed PR domain construct by DNA sequencing at the Plant Biotechnology Institute, National Research Council (Saskatoon, Saskatchewan, Canada). Ligation of the PCR product to the expression vector pET-30a(+) at the NdeI and XhoI sites produced plasmid pET30/PR. Transformation of the plasmid pET30/PR into Escherichia coli (E. coli) BL21(DE3) cells expressed C-terminal His 6 -tagged recombinant PR domain. Over-expression of the PR domain was induced by isopropyl β-D-1-thiogalactopyranoside (IPTG, final concentration of 0.5 mM).

Purification and activity assay of PR domain
In spite of the relatively low yield (1-2 mg purified protein per liter of cell culture), the recombinant PR domain was purified to homogeneous by affinity chromatography using a pre-packed 5-mL HisTrap HP column on an ÄKTA ® Purifier™ FPLC system ( Figure 2). In contrast to normal Histagged protein purifications, purification of the PR domain was undertaken at pH 9.3 instead of pH 7.5 to 8.0, because the PR domain did not bind to the Ni-based HisTrap column at pH 7.5 to 8.0, which was likely due to the high isoelectric point of the PR domain (calculated theoretical pI is 9.2). The purity of the PR domain in the collected fractions was examined on a 16% SDS-PAGE gel ( Figure 2). The fractions (lanes 5-8) containing pure PR domain were combined, concentrated to 1 mg/mL, and stored at -80 ºC. The apparent molecular weight for the PR domain was 24 kDa as determined from the SDS-PAGE gel, consistent with the calculated molecular weight. In order to determine whether the PR domain was active, we measured the methyltransferase activity of the PR domain using a P81 phosphocellulose filter paper based radioactive assay. As shown in Figure 3, the PR domain exhibited a concentration-dependent methyltransferase activity although the correlation between PR domain concentration and the methyltransferase was not quite linear. This indicated that the PR domain was in its active form suitable for X-ray protein crystallographic studies.

Crystallization of PR domain
The initial crystallization conditions for the PR domain of RIZ1 were obtained by the hanging-drop vapor diffusion method using the commercially available Classics, Classics Lite, PEGs, PEGs II and ComPAS crystallization screening suites from Qiagen-Nextal (Mississauga, Ontario, Canada). Needleshaped crystals appeared within 5 days from the crystallization condition with the reservoir solution containing 0.1M HEPES, pH7.5, and 4 M NaCl (Figure 4). The maximum size of the needle-shaped crystals was 0.20 x 0.01 x 0.01 mm. The crystals were capable of being stained into dark blue color using the IZIT Crystal Dye™ from the Hampton Research (California, USA), indicating that they were indeed the crystals of the PR domain. Preliminary X-ray diffraction studies at the Canadian Light Source (Saskatoon, Saskatchewan, Canada) showed that the largest crystal was a twin crystal (data not shown). Further optimization of the crystallization conditions to grow larger and thicker crystals suitable for diffraction data collection is still in progress.

Cloning and expression of the gene fragment encoding the PR domain
Plasmid containing the full-length RIZ gene was kindly provided by Dr. Shi Huang of the Burnham Institute of Medical Research. Using the plasmid DNA as the template and a pair of DNA primers (forward: 5'-CATATGCGCAGACGGACAGCGGATGC-3' with an NdeI restriction site; backward: 5'-CTCGAGGTAGCTGAAGTCCTTCAGCTC-3' with an XhoI restriction site) with their sequences corresponding to the 5' and 3' ends of the gene fragment encoding the PR domain of RIZ1 (residues , respectively, we amplified a DNA fragment of the expected size by PCR reaction on an Eppendorf ® Mastercycler™ personal thermocycler (Eppendorf Canada, Ontario, Canada). The PCR product was cleaned using a QIAquick PCR purification kit (Qiagen Canada, Ontario, Canada) and then reacted with dATP to add adenines at both ends using the Taq DNA polymerase (Invitrogen Canada, Ontario, Canada). The resultant was subject to TA cloning using a TA cloning kit (Invitrogen Canada). The new PCR product was directly ligated to the TA vector pCR2.1 and transformed into E. coli TOP10 cells. Plasmid DNA extracted from positive transformants was digested with restriction endonucleases NdeI and XhoI. The digested DNA fragment was first confirmed to be the gene fragment encoding the PR domain by DNA sequencing and then sub-cloned into the expression vector pET30a(+) at the NdeI and XhoI sites. The resulted plasmid pET30/PR was transformed into E. coli DH5α cells. Plasmid DNA was extracted from positive clones on Luria-Bertani (LB)-agar plates containing kanamycin (30mg/ml) and transformed into E. coli BL21(DE3) cells for over-expression of the PR domain. One liter of LB plus kanamycin medium was inoculated with 1.0 mL of overnight culture from a single colony of transformed E. coli BL21(DE3) cells and incubated at 37 °C with shaking at 250 rpm until OD 600 of the culture reached 0.6. Protein expression was induced by adding IPTG (final concentration of 0.5 mM) to the cell culture. Cell growth was allowed to proceed for an additional 4 hours. The cells were harvested by centrifugation at 5,000 g for 20 minutes and the pellets were stored at -80 °C.

Purification of the PR domain
A frozen pellet from 1 L cell culture was suspended in 40 mL of the lysis buffer (20 mM Tris-HCl, pH 9.3, 0.3 M NaCl, 10 mM imidazole, 0.5 mM phenylmethylsulphonyl fluoride (PMSF), 5 mM βmercaptoethanol (β-ME), 1 μM pepstatin A, and 1 g/L lysozyme) and incubated with gentle rotation for 30 min. The cells were further disrupted by sonication using a Sonifier™ 150 sonicator from Branson Ultrasonics (Danbury, Connecticut, USA). The cell lysate was centrifuged at 20,000 g for 30 min. The supernatant was loaded onto a pre-packed 5-mL HisTrap HP column from GE Healthcare Canada (Baie d'Urfé, Québec, Canada). The column was washed thoroughly with about 100 mL of the wash buffer (20 mM Tris-HCl, pH 9.3, 0.3 M NaCl, 50 mM imidazole, 0.5 mM PMSF, 5 mM β-ME, and 1 μM pepstatin A) at a flow rate of 2 mL/min until the elute UV absorbance was approximately zero. The PR domain was then eluted with 20 mL of the elution buffer (20 mM Tris-HCl, pH 9.3, 0.3 M NaCl, 100 mM imidazole, 0.5 mM PMSF, 5 mM β-ME, and 1 μM pepstatin A). Purity of the PR domain in the elution fractions was examined on a 16% SDS-PAGE gel. Fractions containing pure PR domain were combined, and concentrated to 1 mg/mL, and stored at -80 °C.

Activity assay
The activity of the PR domain was assayed by the methylation reaction. Briefly, the PR domain was added to a 20 μL reaction solution containing 16 μM radio-labeled SAM, 50 mM Tris-HCl, pH 9.0, 5 mM MgCl, 4 mM dithiothreitol (DTT), and 0.125 g/L histone H3 at different concentrations. The reaction solution was incubated at 30 °C for 1 hr before spotting onto a P81 phosphocellulose filter paper. The P81 filter paper was first washed with 10% trichloroacetic acid (15 min) three times and 95% ethanol (1 min) once, then dried, and finally added into a scintillation vial containing 5 mL scintillation cocktail. The radioactivity was measured using a scintillation counter. The result was calibrated with solvent control.

Crystallization of the PR domain
Crystallization of the PR domain was performed by the hanging-drop vapor-diffusion method at room temperature. Initial crystallization conditions were obtained by the sparse-matrix protocol [30]. Needle-shaped crystals were obtained within 5 days from drops containing equal-volume mixtures (2 μL:2 μL) of protein solution (1 mg/mL in 10 mM Tris-HCl, pH 7.7) and reservoir solution (4 M NaCl, 0.1 HEPES, pH 7.5) equilibrated against the reservoir solution (0.6 mL). The crystals were confirmed to be the PR domain by staining with the IZIT Crystal Dye™ from Hampton Research (Aliso Viejo, California, USA).

Conclusion
In the current study, the PR domain (amino acid residues 13-190) of human tumor suppressor RIZ1 was cloned and over-expressed. The recombinant PR domain was confirmed to be active by the methylation reaction and crystallized by the sparse-matrix protocol. The maximum size of the needleshaped crystals was 0.20 x 0.01 x 0.1 mm. Optimization of the initial crystallization conditions to obtain large crystals suitable for X-ray diffraction is in progress.